Abstract
Recommendation models based on rating behavior often fail to properly deal with the problem of data sparsity, resulting in the cold-start phenomenon, which limits the recommendation effect. A model based on user behavior and semantics can better describe user preferences and item features to improve the performance of a recommender system, but is usually shallow and ignores deep features between the user and item. This paper proposes a deep neural network and self-attention mechanism (DSAM) model to solve these problems. The DSAM model introduces a two-way LSTM unit and a self-attention mechanism, combined with a large-scale pretrained BERT model to mine deep nonlinear features and hidden vectors in user comment information and perform score prediction. In comparative experiments carried out on the Amazon product dataset, the error of DSAM prediction results was lower than that of a reference group, and the average error was reduced by 4%.
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Liu, C., Deng, X. (2021). Deep Recommendation Model Based on BiLSTM and BERT. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13032. Springer, Cham. https://doi.org/10.1007/978-3-030-89363-7_30
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DOI: https://doi.org/10.1007/978-3-030-89363-7_30
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